Preoperative evaluation : risk management and implementation aspects

Abstract

In preoperative risk management the anesthesiologist uses diagnostic information to estimate the probability of outcomes and to decide on the anesthetic strategy in a particular patient. The aim of this thesis was explore to what extent simple patient characteristics, particularly obtained from preoperative patient history and physical examination, could contribute to preoperative risk management. Furthermore, the implementation of outpatient preoperative evaluation (OPE) clinics in the Netherlands as well as the effects of OPE in a particular hospital were quantified. Preferably, during OPE, ‘healthy’ patients are easily distinguished from the remainder using a minimal but optimal set of diagnostic tests. Currently, the literature shows no evidence about the optimal contents of preoperative evaluation. To determine the optimal set of diagnostic tests to appropriately detect existing co-morbidity would require an empirical diagnostic study. In such a study the contribution of each diagnostic test is related to the diagnostic outcome, i.e. ‘presence or absence of significant co-morbidity’. To demonstrate how diagnostic research might be used in the clinical setting of an OPE clinic, we studied the diagnostic value of cardiac auscultation to detect VHD. Diagnostic research will decrease redundant information, but requires the a priori definition of what constitutes significant co-morbidity. The anesthesiologist should also have evidence-based knowledge about the probability of perioperative complications and to what extent the anesthetic strategy may alter the complication rate. Prognostic prediction studies aim to estimate the probability of future occurrence of a particular outcome in a particular patient and are also suitable to estimate to what extent the individual risk of a patient can be modified using pre-emptive strategies, such as administering erythropoietin before surgery. Before a prediction model can be implemented in practice, its generalizability (the application to patients from a different but related population) should be estimated. To obtain an estimate of the generalizability, we applied two prediction models on perioperative blood transfusion to a patient population from another hospital. Both models stayed robust and we concluded that they could be implemented in practice. In this context, there will be an important role for information technology: a complication registration system could provide the necessary data for continuous prognostic prediction research, which in turn will provide risk stratification systems for (long-term) morbidity and mortality to be built-in in electronic patient record software used at the OPE clinic. There are several potential benefits of OPE. For example, OPE allows for comprehensive assessment and optimization of the patient’s health condition without delaying surgery. However, to extract the maximal benefits from OPE the incentives for all those concerned in preoperative patient care, such as anesthesiologists and surgical specialists, must be clear to change existing practice patterns, such as routine admission of patients to the ward the day before surgery. Because widespread implementation of OPE will require an increase in the number of anesthesiologists, the questions arises whether a specially trained anesthetic nurse can screen patients adequately. The partial substitution of the anesthesiologist by a specially trained nurse in a ‘mixed-provider model OPE clinic’ could have several benefits and might increase the quality and cost-effectiveness of OPE.